Genomic Prediction: Progress and Perspectives for Rice Improvement
Overview
Affiliations
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
A Feature Engineering Method for Whole-Genome DNA Sequence with Nucleotide Resolution.
Wang T, Cui Y, Sun T, Li H, Wang C, Hou Y Int J Mol Sci. 2025; 26(5).
PMID: 40076901 PMC: 11899767. DOI: 10.3390/ijms26052281.
Gene Pyramiding Strategies for Sink Size and Source Capacity for High-Yield Japonica Rice Breeding.
Ueda T, Taniguchi Y, Adachi S, Shenton M, Hori K, Tanaka J Rice (N Y). 2025; 18(1):6.
PMID: 39945924 PMC: 11825427. DOI: 10.1186/s12284-025-00756-w.
Stochastic simulation to optimize rice breeding at IRRI.
Seck F, Prakash P, Covarrubias-Pazaran G, Gueye T, Diedhiou I, Bhosale S Front Plant Sci. 2024; 15:1488814.
PMID: 39554523 PMC: 11563958. DOI: 10.3389/fpls.2024.1488814.
Montesinos-Lopez O, Pulido-Carrillo C, Montesinos-Lopez A, Larios Trejo J, Montesinos-Lopez J, Agbona A Genes (Basel). 2024; 15(8).
PMID: 39202329 PMC: 11353568. DOI: 10.3390/genes15080969.
Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.
Bartholome J, Ospina J, Sandoval M, Espinosa N, Arcos J, Ospina Y PLoS One. 2024; 19(8):e0307009.
PMID: 39173048 PMC: 11341055. DOI: 10.1371/journal.pone.0307009.